This article is the last in a three-part series about using analytics to drive documentation efforts and achieve business goals.
In the first part of this series, we covered the importance of learning about how your documentation is performing, who’s consuming it, and how it’s being used. And in the second part, we explained what makes data meaningful, distinguished between three types of data, and showed how goals can add direction to your data.
Now let’s put theory into action and see how you can turn seemingly benign data into actions that support your content strategy.
Turning data into action
When you combine actionable data with directed goals, you’re ready to take concrete and measurable actions.
To put it simply: Actionable data + directed goals = clear action
Anyone can make use of this formula, from product teams working to improve user retention, to support teams aiming to reduce call-center costs, to marketing teams looking to drive more traffic and leads.
Once you understand which data helps drive your content forward, you’ve got yourself a solid plan of action. So let’s dig in and see how you can use two analytics to drive concrete action.
A simple case
Say you’re planning a round of content updates. You have thousands of pages of product content and resources are limited, so you’ll need to figure out which content is most in need of updating. How will you prioritize this enormous effort?
This is the actionable data you have at your disposal:
- Most popular topics
- Content aging
You can combine these two pieces of data by plotting a topic’s age (or freshness) on the horizontal axis, and popularity (or page views) on the vertical axis. When you combine this data in an innovative way, your content can be split into one of four quadrants as displayed below:
Each one of these quadrants is loaded with insight and action:
- Imagine you have lots of old content that no one is reading. Your call to action would be to retire it.
- What if you have spent time and effort to create new content, but no one is reading it? You should explore why this may be the case. What can you do to resolve the issue? Do you need to improve the navigation to ensure people can find it?
- Perhaps everyone is reading your old content. Why is it so much more compelling than your newer content? Is it a problem of search? Of not surfacing the new content? Or perhaps everyone is still using legacy versions of the product, and you’re missing out on huge upselling opportunities?
- Finally, we have the possibility of everyone arriving at your new content. In most organizations, this might be the most attractive quadrant to inhabit.
To be clear, these quadrants have meaning and direction only if we assume that our directed goal is that customers should read only up-to-date content. You can support a variety of other goals by using different analytics and creating cross-sections of them.
This is a clear illustration that by combining the power of actionable data with directed goals, you can gain many valuable insights that have far-reaching impacts within your organization.
We’ve now explored why you should prioritize data collection, what makes data useful, and what makes it actionable. And we’ve seen how pairing actionable data with directed goals can lead to concrete content initiatives.
As you and your team dive into a data-driven future, here are some key steps to follow:
- Educate your team and other stakeholders about the value of data-driven content strategy.
- Understand whether your current content infrastructure allows for complete and accurate data collection.
- Determine your team’s content goals, establish measurable KPIs, and ensure they’re aligned with overall company goals.
- Begin collecting data and turn it into actionable data by pairing it with content KPIs.
- Based on #4, develop your content strategy and act on it.
- Use analytics to measure the impact of your changes and further refine your content.
With the right tools to gather actionable insights, you can finally begin to analyze content performance, explore user behavior and interests, and better promote your team’s KPIs. And ultimately, by making data-driven decisions, you’ll make it easier for users to find the product answers they’re looking for.
If you’d like help building a data-driven strategy for your team, from getting organizational buy-in to preparing your content infrastructure for data collection, please be in touch and I’d be happy to chat.
Want to hear Lawrence Orin and Joe Gelb discuss these ideas in depth? Watch our on-demand webinar about creating data-driven documentation.
Lawrence Orin is Product Evangelist and Customer Implementation Expert at Zoomin, where he lends his experience in documentation to help new customers with implementation, create their taxonomies, and develop their content strategy. He previously led documentation teams at Radvision and Riverbed, in addition to heading up other teams in technical support and customer services.
This article is an excerpt from “Becoming a Data-Driven Documentation Team” which was published in the December 2019 issue of Best Practices, a publication of CIDM.